[Paper Review] An Energy Efficient Routing Algorithm for Wireless Sensor Networks Using Mobile Sensors

Meysam Yari, Parham Hadikhani+3 more|arXiv (Cornell University)|Mar 6, 2021

Energy Efficient Wireless Sensor Networks10 references4 citations

TL;DR

This paper proposes an energy-efficient routing algorithm for wireless sensor networks using mobile sensors to enhance energy balance and prolong network lifetime. The method employs a four-phase process—hole prevention, parameter update, hole detection, and hole coverage using static or mobile nodes—demonstrating improved performance over conventional routing protocols in simulations.

ABSTRACT

The increasing usage of wireless sensor networks in human life is an indication of the high importance of this technology. Wireless sensor networks have a vast majority of applications in monitoring and care which are known as target tracking. In this application, the moving targets are monitored and tracked in the environment. One of the most important challenges in this area is the limited energy of the sensors. In this paper, we proposed a new algorithm to reduce energy consumption by increasing the load balancing in the network. The proposed algorithm consists of four phases. In the first phase, which is the hole prevention phase, in each cluster, it is checked by the cluster heads that if the energy level in an area of the cluster is less than the threshold, a mobile node is sent to that area. The second phase is the update phase. In this phase, the parameters required to detect a hole are updated. In the third phase, the hole in the cluster is detected, and in the fourth phase, the hole is covered by static or moving nodes. A comparison of simulation results with the well-known and successful routing method in wireless sensor networks show that the proposed method is suitable and working properly.

Motivation & Objective

• Address the critical challenge of limited energy in wireless sensor networks (WSNs), particularly in target tracking applications.
• Reduce energy consumption and prevent network partitioning due to energy holes caused by uneven energy depletion.
• Improve network longevity by leveraging mobile sensors to dynamically balance energy load across clusters.
• Ensure reliable coverage and connectivity in monitored areas through adaptive deployment of mobile nodes.
• Develop a routing mechanism that integrates mobility with cluster-based architecture for enhanced energy efficiency.

Proposed method

• In the hole prevention phase, cluster heads monitor local energy levels and dispatch mobile nodes to low-energy areas when thresholds are breached.
• The update phase dynamically refreshes parameters such as residual energy and node density to maintain accurate hole detection.
• Hole detection is performed using a threshold-based mechanism that identifies regions with insufficient coverage or energy.
• In the hole coverage phase, either static or mobile nodes are deployed to restore connectivity and energy balance in affected areas.
• The algorithm operates in a cluster-based architecture where cluster heads coordinate mobility and energy management decisions.
• Energy load balancing is achieved by redirecting data traffic and deploying mobile nodes to underutilized or depleted regions.

Experimental results

Research questions

• RQ1 How can energy holes in wireless sensor networks be proactively detected and mitigated using mobile sensors?
• RQ2 To what extent does integrating mobile nodes into cluster-based routing improve energy balance and network lifetime?
• RQ3 What is the impact of dynamic hole coverage on data delivery reliability and energy efficiency in target tracking applications?
• RQ4 How does the proposed algorithm compare to established routing protocols in terms of energy consumption and network longevity?
• RQ5 Can mobility-based load balancing reduce the risk of premature network partitioning in energy-constrained WSNs?

Key findings

• The proposed algorithm effectively prevents energy holes by proactively deploying mobile sensors to low-energy regions.
• Simulation results show improved energy efficiency and extended network lifetime compared to conventional routing protocols.
• The method achieves better load balancing by distributing data traffic and node deployment based on real-time energy monitoring.
• The integration of mobile nodes significantly enhances coverage reliability and reduces energy hotspots in cluster heads.
• The four-phase approach ensures timely detection and recovery from energy holes, maintaining network connectivity.
• The algorithm demonstrates robust performance in dynamic environments typical of target tracking applications.